The present invention relates generally to computerized medical systems for diagnosing the human body and, more particularly, concerns procedures for analyzing images of a volume containing the spine to isolate the spine from its surroundings and to isolate the various components of the spine.
Osteoporosis is a condition of decreased bone mass that leads to fractures and negatively impacts millions of people, causing immobility, pain, and mortality, and costing billions of dollars annually. Although an effective technique (DXA) for early diagnosis exists and the condition is treatable, screening compliance is low. Preliminary diagnosis of osteoporosis is achievable by inspecting vertebral bodies in images, usually CT scans performed for other clinical reasons, based on morphology, structure, and density. However, this is time consuming and the condition is frequently under diagnosed.
Diagnosis involves segmentation of the spine images to identify and isolate the various components of the spine. In most clinical settings spine segmentation is a manual operation performed by a trained radiologist. This methodology is not amenable to quantitative anatomical analysis and relies upon the radiologists experience to identify abnormalities in imaging studies. In cases where quantitative analysis on a spine is performed, the segmentation process is typically manual or semi-automatic and requires user intervention in various stages of the segmentation, such as spine region isolation, seed positioning and iterative refinements.
A process is needed to detect osteoporosis findings in CT scans. As the number of CTs performed in a medical setting is typically large the process should be fully automated. As the CTs are not performed specifically for osteoporosis detection, no additional, constraints may be imposed (i.e. there is no ability to add phantoms to the scan). In addition, die process should be resilient to imaging protocol variations and anatomical variations and abnormalities.
In order to perform automated quantitative analysis of the vertebral body, a segmentation building block is required that accepts a CT study as input and produces a segmentation of the vertebral bodies as output. Challenges in vertebral body segmentation result from variable input data caused by anatomical abnormalities and variations, study protocol and scanner. Examples, of anatomical abnormalities and variations include low density vertebral cortex, fatty trabecular bone, vascular calcifications osteophytes, scoliosis, calcified longitudinal ligaments, prominent epidural veins, focal disc calcifications, and noise enhancers such as obesity and metal hardware. Examples of study protocol variations include contrast in the vascular and digestive systems and patient position and orientation. Examples of scanner variations include radiation dose, resolution, and scanner table artifacts.
In accordance with one aspect of the invention, a computerized system receives as input a digital representation of a volume region of a patient's body containing the spine. Typically, but not necessarily, the representation is in the form of a three-dimensional DICOM or MHA representation of a CT imaging study. The system analyzes the volume using statistical, heuristic and other computational techniques, isolates the spine from the surrounding volume, and segments each vertebral body trabecular bone, each vertebral body cortex, each intervertebral disc, and locates the planes of the intervertebral discs. A label map is produced that corresponds to the input volume and classifies each voxel, as either background or as part of a specified spine element.
A system in accordance with the present invention provides fully automated vertebral body segmentation, eliminating the labor intensive portions of the segmentation process and enabling a broad range of clinical and research scenarios which were previously limited by the availability of trained professionals to perform the segmentation. As an example, it makes possible the screening of large quantities of imaging studies using quantitative analyses of the automatically generated vertebral segmentations produced in accordance with the present invention.